{"id":"W4236777019","doi":"10.32920/ryerson.14651574","title":"Matching-based cache placement decision for 5G network caching","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Cache; Backhaul (telecommunications); Cache algorithms; Cache invalidation; Cache pollution; Bottleneck; Computer network; Matching (statistics); Smart Cache; Page cache; Parallel computing; CPU cache; Mathematics; Base station","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001141954,0.0003984153,0.0004794724,0.0001184267,0.0003330332,0.001082303,0.001561246,0.0002677982,0.00002702424],"category_scores_gemma":[0.00006130984,0.0003684076,0.0004837158,0.000155716,0.00001512266,0.0001648534,0.001135798,0.0006724963,0.0000125947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002028322,"about_ca_system_score_gemma":0.0004525989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008948281,"about_ca_topic_score_gemma":0.0003665538,"domain_scores_codex":[0.9971302,0.00013907,0.0005130304,0.001142741,0.000535955,0.0005390571],"domain_scores_gemma":[0.9972159,0.0007520085,0.0002212439,0.001475351,0.000182314,0.0001531846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002981692,0.0000736728,0.00007218198,0.00009546628,0.00006538948,0.00002372847,0.0002849445,0.9742645,0.00007292761,0.003082377,0.004538389,0.01739659],"study_design_scores_gemma":[0.0007462894,0.00007040326,0.00009418016,0.0007900755,0.00004175329,0.000007235703,0.00009764271,0.9871944,0.000227603,0.008731724,0.001344165,0.0006545326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03380714,0.0006231093,0.9591924,0.0007348591,0.003671938,0.0005648221,0.000006816492,0.0003905762,0.001008356],"genre_scores_gemma":[0.834983,0.00001915795,0.1616552,0.00206882,0.0004319744,0.0001453244,0.00009215908,0.0000319215,0.000572394],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8011758,"threshold_uncertainty_score":0.9999546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02924919318287178,"score_gpt":0.2690654106091593,"score_spread":0.2398162174262875,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}